Hybrid Pepper Plant Disease and Nutrition Deficiency Diagnosis with UNet and CNN

Black pepper is an essential commodity in Malaysia, which not only adds flavors to local dishes but also financially supports the local community and the country’s economy. However, black pepper plants are susceptible to various diseases and nutrient deficiency, which affect their growth and pepperc...

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Main Author:	Olivia Ching Hui, Chen
Format:	Thesis
Language:	English English English
Published:	2024
Subjects:	T Technology (General)
Online Access:	http://ir.unimas.my/id/eprint/46437/3/Letter%20Consent%20For%20Restricted%20Use_OLIVIA%20CHEN%20CHING%20HUI.pdf http://ir.unimas.my/id/eprint/46437/4/Thesis%20Ms._OLIVIA%20CHEN%20CHING%20HUI.%20restricted%20-%206%20pages.pdf http://ir.unimas.my/id/eprint/46437/7/Olivia%20Chen%20Ching%20Hui%20ft.pdf
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id	my-unimas-ir.46437
record_format	uketd_dc
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English English English
topic	T Technology (General)
spellingShingle	T Technology (General) Olivia Ching Hui, Chen Hybrid Pepper Plant Disease and Nutrition Deficiency Diagnosis with UNet and CNN
description	Black pepper is an essential commodity in Malaysia, which not only adds flavors to local dishes but also financially supports the local community and the country’s economy. However, black pepper plants are susceptible to various diseases and nutrient deficiency, which affect their growth and peppercorn production, and it is costly for the farmers and the officers from the Malaysian Pepper Board to provide timely aid. In the meantime, even though various methods have been attempted to support the local farmers, there are still difficulties for black pepper farmers in accurately diagnosing the problems in their crops. Besides that, many computational tools and convolutional neural network (CNN) models are available for plants in the PlantVillage dataset, excluding the black pepper plant. Therefore, this study aims to design a CNN model that accurately diagnoses both single and hybrid cases of black pepper diseases and nutrient deficiencies which enhance detection accuracy and robustness, then to develop and to test a robust hybrid detection model that identifies the most commonly co-occuring symptom, red rust disease, using UNet segmentation and fine-tuned CNN models, and to test the new CNN model with other existing models. The result shows that EfficientNet is the best performing black pepper plant disease and nutrient deficiency classification model, followed by ResNet, MobileNet, and DenseNet. For the custom CNN with five convolutional pooling layer blocks with 0.2 dropout performed almost as well as the state-of-the-art model. Besides that, UNet was also utilized to segment the regions with the secondary disease, red rust disease, from the images, and the segmented results again fit into EfficientNet to cross-check if they were red rust or not. Therefore, the whole project involves CNNs classifying the pictures of the leaves into their respective classes, whereby UNet and EfficientNet are used to identify and check the presence of red rust in those images.
format	Thesis
qualification_level	Master's degree
author	Olivia Ching Hui, Chen
author_facet	Olivia Ching Hui, Chen
author_sort	Olivia Ching Hui, Chen
title	Hybrid Pepper Plant Disease and Nutrition Deficiency Diagnosis with UNet and CNN
title_short	Hybrid Pepper Plant Disease and Nutrition Deficiency Diagnosis with UNet and CNN
title_full	Hybrid Pepper Plant Disease and Nutrition Deficiency Diagnosis with UNet and CNN
title_fullStr	Hybrid Pepper Plant Disease and Nutrition Deficiency Diagnosis with UNet and CNN
title_full_unstemmed	Hybrid Pepper Plant Disease and Nutrition Deficiency Diagnosis with UNet and CNN
title_sort	hybrid pepper plant disease and nutrition deficiency diagnosis with unet and cnn
granting_institution	Faculty of Cognitive Sciences and Human Development
granting_department	Cognitive Science
publishDate	2024
url	http://ir.unimas.my/id/eprint/46437/3/Letter%20Consent%20For%20Restricted%20Use_OLIVIA%20CHEN%20CHING%20HUI.pdf http://ir.unimas.my/id/eprint/46437/4/Thesis%20Ms._OLIVIA%20CHEN%20CHING%20HUI.%20restricted%20-%206%20pages.pdf http://ir.unimas.my/id/eprint/46437/7/Olivia%20Chen%20Ching%20Hui%20ft.pdf
_version_	1818611700125925376
spelling	my-unimas-ir.464372024-12-03T02:00:15Z Hybrid Pepper Plant Disease and Nutrition Deficiency Diagnosis with UNet and CNN 2024-10-20 Olivia Ching Hui, Chen T Technology (General) Black pepper is an essential commodity in Malaysia, which not only adds flavors to local dishes but also financially supports the local community and the country’s economy. However, black pepper plants are susceptible to various diseases and nutrient deficiency, which affect their growth and peppercorn production, and it is costly for the farmers and the officers from the Malaysian Pepper Board to provide timely aid. In the meantime, even though various methods have been attempted to support the local farmers, there are still difficulties for black pepper farmers in accurately diagnosing the problems in their crops. Besides that, many computational tools and convolutional neural network (CNN) models are available for plants in the PlantVillage dataset, excluding the black pepper plant. Therefore, this study aims to design a CNN model that accurately diagnoses both single and hybrid cases of black pepper diseases and nutrient deficiencies which enhance detection accuracy and robustness, then to develop and to test a robust hybrid detection model that identifies the most commonly co-occuring symptom, red rust disease, using UNet segmentation and fine-tuned CNN models, and to test the new CNN model with other existing models. The result shows that EfficientNet is the best performing black pepper plant disease and nutrient deficiency classification model, followed by ResNet, MobileNet, and DenseNet. For the custom CNN with five convolutional pooling layer blocks with 0.2 dropout performed almost as well as the state-of-the-art model. Besides that, UNet was also utilized to segment the regions with the secondary disease, red rust disease, from the images, and the segmented results again fit into EfficientNet to cross-check if they were red rust or not. Therefore, the whole project involves CNNs classifying the pictures of the leaves into their respective classes, whereby UNet and EfficientNet are used to identify and check the presence of red rust in those images. - 2024-10 Thesis http://ir.unimas.my/id/eprint/46437/ http://ir.unimas.my/id/eprint/46437/3/Letter%20Consent%20For%20Restricted%20Use_OLIVIA%20CHEN%20CHING%20HUI.pdf text en validuser http://ir.unimas.my/id/eprint/46437/4/Thesis%20Ms._OLIVIA%20CHEN%20CHING%20HUI.%20restricted%20-%206%20pages.pdf text en validuser http://ir.unimas.my/id/eprint/46437/7/Olivia%20Chen%20Ching%20Hui%20ft.pdf text en validuser masters Faculty of Cognitive Sciences and Human Development Cognitive Science Malaysian Pepper Board Adama, A., Ee, K. P., Sahari, N., Tida, A., Shang, C. Y., Tawie, K. M., Kamarudin, S., & Mohamad, H. (2018). Dr. LADA: Diagnosing black pepper pest and diseases with decision tree. 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